A Computational Model for Overcoming Drug Resistance Using Selective Dual-Inhibitors for Aurora Kinase A and Its T217D Variant

The human Aurora kinase-A (AK-A) is an essential mitotic regulator that is frequently overexpressed in several cancers. The recent development of several novel AK-A inhibitors has been driven by the well-established association of this target with cancer development and progression. However, resistance and cross-reactivity with similar kinases demands an improvement in our understanding of key molecular interactions between the Aurora kinase-A substrate binding pocket and potential inhibitors. Here, we describe the implementation of state-of-the-art virtual screening techniques to discover a novel set of Aurora kinase-A ligands that are predicted to strongly bind not only to the wild type protein, but also to the T217D mutation that exhibits resistance to existing inhibitors. Furthermore, a subset of these computationally screened ligands was shown to be more selective toward the mutant variant over the wild type protein. The description of these selective subsets of ligands provides a unique pharmacological tool for the design of new drug regimens aimed at overcoming both kinase cross-reactivity and drug resistance associated with the Aurora kinase-A T217D mutation

Roughness of Molecular Property Landscapes and Its Impact on Modellability

In molecular discovery and drug design, structure–property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks

Toward a Standard Protocol for Micelle Simulation

In this paper, we present protocols for simulating micelles using dissipative particle dynamics (and in principle molecular dynamics) that we expect to be appropriate for computing micelle properties for a wide range of surfactant molecules. The protocols address challenges in equilibrating and sampling, specifically when kinetics can be very different with changes in surfactant concentration, and with minor changes in molecular size and structure, even using the same force field parameters. We demonstrate that detection of equilibrium can be automated and is robust, for the molecules in this study and others we have considered. In order to quantify the degree of sampling obtained during simulations, metrics to assess the degree of molecular exchange among micellar material are presented, and the use of correlation times are prescribed to assess sampling and for statistical uncertainty estimates on the relevant simulation observables. We show that the computational challenges facing the measurement of the critical micelle concentration (CMC) are somewhat different for high and low CMC materials. While a specific choice is not recommended here, we demonstrate that various methods give values that are consistent in terms of trends, even if not numerically equivalent